Wind Power Long-Term Scenario Generation Considering Spatial-Temporal Dependencies in Coupled Electricity Markets
1
Institute for Research in Technology (IIT), ICAI School of Engineering, Comillas Pontifical University, 28015 Madrid, Spain
2
Santander Big Data Institute, Universidad Carlos III de Madrid, 28903 Getafe, Spain
*
Author to whom correspondence should be addressed.
Energies 2020, 13(13), 3427; https://doi.org/10.3390/en13133427
Received: 5 June 2020
/
Revised: 26 June 2020
/
Accepted: 30 June 2020
/
Published: 3 July 2020
(This article belongs to the Section B2: Wind, Wave and Tidal Energy)
Wind power has been increasing its participation in electricity markets in many countries around the world. Due to its economical and environmental benefits, wind power generation is one of the most powerful technologies to deal with global warming and climate change. However, as wind power grows, uncertainty in power supply increases due to wind intermittence. In this context, accurate wind power scenarios are needed to guide decision-making in power systems. In this paper, a novel methodology to generate realistic wind power scenarios for the long term is proposed. Unlike most of the literature that tackles this problem, this paper is focused on the generation of realistic wind power production scenarios in the long term. Moreover, spatial-temporal dependencies in multi-area markets have been considered. The results show that capturing the dependencies at the monthly level could improve the quality of scenarios at different time scales. In addition, an evaluation at different time scales is needed to select the best approach in terms of the distribution functions of the generated scenarios. To evaluate the proposed methodology, several tests have been made using real data of wind power generation for Spain, Portugal and France.
View Full-Text
Keywords:
ARIMA; long-term forecasting; multi-area electricity markets; SARIMA; wind power forecasting
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Marulanda, G.; Bello, A.; Cifuentes, J.; Reneses, J. Wind Power Long-Term Scenario Generation Considering Spatial-Temporal Dependencies in Coupled Electricity Markets. Energies 2020, 13, 3427. https://doi.org/10.3390/en13133427
AMA Style
Marulanda G, Bello A, Cifuentes J, Reneses J. Wind Power Long-Term Scenario Generation Considering Spatial-Temporal Dependencies in Coupled Electricity Markets. Energies. 2020; 13(13):3427. https://doi.org/10.3390/en13133427
Chicago/Turabian StyleMarulanda, Geovanny, Antonio Bello, Jenny Cifuentes, and Javier Reneses. 2020. "Wind Power Long-Term Scenario Generation Considering Spatial-Temporal Dependencies in Coupled Electricity Markets" Energies 13, no. 13: 3427. https://doi.org/10.3390/en13133427
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
